In many military, communication and security applications, it is desirable to compress image data to reduce the bandwidth requirements of wireless and networked-based systems over which the image data is transmitted. By reducing the amount of data associated with these images, the amount of storage space required can also be minimized, allowing long image sequences to be stored on a single disk.
The most widely used image-compression methods are JPEG, MJPEG, MPEG, and H.264, which are block-based techniques. These methods operate by transforming the original image using a discrete cosine transform (DCT), then quantizing the transform coefficients. The DCT separates the image into parts (or spectral sub-bands) of differing importance with respect to the image's visual quality, transforming the image from the spatial domain to the frequency domain. To decompress these images, most commercially-available hardware and software implementations use the inverse DCT. To achieve large amounts of compression, block transform coding is generally lossy, meaning that image information is permanently discarded during compression so that the original image cannot be perfectly reconstructed from the compressed version. In the process of compression and decompression, the 8×8 matrices of sub-image blocks associated with the transform often produce image block artifacts, where the outlines of the encoding blocks are superimposed on the image as distinct transitions from one block to another. When the amount of compression is low, the loss of information is slight and unobjectionable. However, at higher compression levels, the information loss becomes increasingly apparent and is associated with the occurrence of visible artifacts relating to the block nature of the encoding and to the quantization of DCT coefficients. JPEG attempts to exploit certain features of human vision, which perceives less detail in color than in brightness, and so encodes chrominance in larger blocks than those used for luminance. This technique leads to additional artifacts at high compression. Because each sub-block (and each macroblock) is processed independently, a critical portion of the image data that connects neighboring blocks is often lost and superfluous edges and discontinuities appear at the block boundaries.
In addition to block artifacts, since the transform data is quantized, information is lost such that the content of the block cannot be reproduced accurately. These “mosquito artifacts”, or “ringing”, appear as an aura or halo around objects.
A number of adaptive filtering methods have been developed for reduction of block artifacts and ringing. A few examples of such methods are provided in U.S. Pat. No. 6,636,645 of Yu; U.S. Pat. No. 7,076,113 of Le Dinh, and U.S. Pat. No. 7,136,536 of Andersson. While these filtering methods have been successful in reducing the artifacts, in some cases, additional features (artifacts) can be added, or data is lost.
The PIXON® method, disclosed in U.S. Pat. No. 5,912,993, and U.S. Pat. No. 6,895,125, incorporated herein by reference, was originally developed for image reconstruction. In such applications the PIXON® method provides superior performance relative to competing methods, providing enhanced spatial resolution and reduced artifacts in the reconstructed image. These benefits can be traced to the PIXON® method's minimum complexity model for the reconstructed image.
The PIXON® image reconstruction scheme built its minimum complexity model by expressing the reconstructed image as a convolution of a pseudo-image with PIXON® kernels that had spatially variable size and shape. The meaning of “minimum complexity” is context dependent. When one is trying to build a minimum complexity hypothesis (model) to answer certain questions with regard to the data (the context), the minimum complexity hypothesis (model) is that which is least informative about the answers to those questions. In the case of images, since the questions of interest are typically: (a) what are the shapes of objects; (b) where are the objects located, and (c) what is the flux density of emission, etc. The minimum complexity model (least informative hypothesis) is the smoothest image consistent with the data. This smoothest image provides the least amount of information on the shapes of objects, their exact location, and the flux density is as spread out as possible. Hence, one can guarantee that with regard to these questions, the data will not be over interpreted. Such a model also automatically eliminates reconstruction artifacts and increases sensitivity and resolution since the artifact level is maximally reduced.
In the PIXON® method, the pseudo-image and PIXON® kernels are allowed to vary on a pixel-by-pixel basis. While this reduces the information content of the reconstructed image dramatically by causing adjacent pixels to be strongly correlated (they were no longer independent numbers), the method goes in the opposite direction of image compression since, for each image, one must specify not only the pseudo-image at each pixel, but the choice of a multitude of PIXON® kernels. In other words, writing down the image directly is much terser than describing the method by which this image was obtained. Therefore, this choice of language for describing the image is not useful for simplifying the image description relative to writing down the image itself.
Nonetheless, given its superior performance for image reconstruction, it would be desirable to adapt the PIXON® method for use in image compression and decompression. The present invention is directed to such a method.